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Exploratory visual sequence mining based on pattern-growth
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-4761-8601
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering.
2019 (English)In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 25, no 8, p. 2597-2610Article in journal (Refereed) Published
Abstract [en]

Sequential pattern mining finds applications in numerous diverging fields. Due to the problem's combinatorial nature, two main challenges arise. First, existing algorithms output large numbers of patterns many of which are uninteresting from a user's perspective. Second, as datasets grow, mining large number of patterns gets computationally expensive. There is, thus, a need for mining approaches that make it possible to focus the pattern search towards directions of interest. This work tackles this problem by combining interactive visualization with sequential pattern mining in order to create a "transparent box" execution model. We propose a novel approach to interactive visual sequence mining that allows the user to guide the execution of a pattern-growth algorithm at suitable points through a powerful visual interface. Our approach (1) introduces the possibility of using local constraints during the mining process, (2) allows stepwise visualization of patterns being mined, and (3) enables the user to steer the mining algorithm towards directions of interest. The use of local constraints significantly improves users' capability to progressively refine the search space without the need to restart computations. We exemplify our approach using two event sequence datasets; one composed of web page visits and another composed of individuals' activity sequences.

Place, publisher, year, edition, pages
2019. Vol. 25, no 8, p. 2597-2610
Keywords [en]
Sequential pattern mining, interactive mining, visual data mining, mining with constraints
National Category
Computer and Information Sciences Media and Communication Technology
Identifiers
URN: urn:nbn:se:liu:diva-153938DOI: 10.1109/TVCG.2018.2848247ISI: 000473597800007PubMedID: 29994660OAI: oai:DiVA.org:liu-153938DiVA, id: diva2:1280734
Note

Funding agencies: CENIIT, Center for Industrial Information Technology at Linkoping University; RESKILL project - public research and innovation funds from the Swedish Transport Administration; Swedish Maritime Administration; Swedish Air Navigation Service Provider LFV

Available from: 2019-01-20 Created: 2019-01-20 Last updated: 2019-07-19

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Vrotsou, KaterinaNordman, Aida

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf